Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Xiaomi Mobile Software Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Beijing Tsingmicro Intelligent Technology Co., Ltd.

Data Enlighten Technology (beijing) Co., Ltd.

Willand Technology Co., Ltd.

Kai Zhou

Qian Wang

Faen Zhang

Yuanhao Xiao

Kun Liu

Lan Zhe Liu

Dongze Xu

Tianhan Xu

Jiayuan Sun

Tao Yu

Hongzhi Jin

Wei Li

Bo Wang

Jiaxing Wang

Jing Yu

Gaocai Han

Jiangliang Guo

Heng Zhang

Shouyi Yin

Peng Ouyang

Xu Yan

Jin Tang

Shiming Yin

Tianning Yu

Kai He

Quan Sun

Wanyang Qiao

Shibin Tang

Xiudong Li

Jian Kang

Rongbing Zhang

Minhao Wang

Fengzhi Qiu

Guming Gao

A method for sharing a resource, includes: generating, by a first AI node, an association relation between the first AI node and at least one other AI node, in which the association relation is configured to generate an AI federal network; receiving, by the first AI node, resource sharing information; determining, by the first AI node, a target AI node of sharing resources with the first AI node based on the resource sharing information; determining, by the first AI node, a target resource to be shared based on the resource sharing information; and sharing, by the first AI node, the target resource with the target AI node through the AI federal network. Therefore, AI nodes realize resource sharing through the AI federal network, which is suitable for resource sharing scenarios of multiple different AI nodes and has good scalability, thereby greatly reducing the development difficulty and cost of resource sharing.

Method for sharing resource, method for creating service, electronic device, and storage medium

Provided is a vehicle accident loss assessment risk control method and apparatus. The method can include: acquiring a vehicle accident loss assessment report of a current accident of a vehicle, wherein the vehicle accident loss assessment report includes vehicle information of the vehicle and vehicle loss items related to the current accident of the vehicle; calculating the vehicle loss items in the vehicle accident loss assessment report based on a pre-established risk algorithm model in combination with the vehicle information, and identifying risk items with risks in the vehicle loss items; and intercepting the risk items or prompting the risk items to a user. According to the embodiment, an early warning can be automatically made to a leakage that may occur in the vehicle accident loss assessment based on artificial intelligence, industry big data standardization, big data mining analysis and the like, to realize the risk control and ensure the accuracy of vehicle accident loss assessment.

Vehicle accident loss assessment risk control method and apparatus, electronic device and storage medium

An image correction method includes: capturing speckle patterns on two planes at different distances to obtain a first image of speckle projected on a first plane and a second image of speckle projected on a second plane; matching the first image with the second image to obtain sub-pixel matching points; obtaining, based on first physical coordinates of the sub-pixel matching points on the first image and second physical coordinates of the sub-pixel matching points on the second image, a mapping matrix between the first and second physical coordinates; obtaining a direction vector of a center of the speckle projector in a camera reference frame according to the mapping matrix; adjusting coordinate axis directions of the camera reference frame to align a horizontal axis direction with the direction vector, updating an imaging matrix of the camera; and mapping a target scene image through the imaging matrix to obtain a corrected image.

Image correction method and apparatus for camera

The present disclosure discloses a method and apparatus for scheduling a resource for a deep learning framework. The method can comprise: querying statuses of all deep learning job objects from a Kubernetes platform at a predetermined interval; and submitting, in response to finding from the queried deep learning job objects a deep learning job object having a status conforming to a resource request submission status, a resource request to the Kubernetes platform to schedule a physical machine where the Kubernetes platform is located to initiate a deep learning training task. The method can completely automate the allocation and release on the resource of the deep learning training task.

Method and apparatus for scheduling resource for deep learning framework

The disclosure discloses a method and apparatus for updating a deep learning model. An embodiment of the method comprises: executing following updating: acquiring a training dataset under a preset path, training a preset deep learning model based on the training dataset to obtain a new deep learning model; updating the preset deep learning model to the new deep learning model; increasing training iterations; determining whether a number of training iterations reaches a threshold of training iterations; stopping executing the updating if the number of training iterations reaches the threshold of training iterations; and continuing to execute the updating after an interval of a preset time length if the number of training iterations fails to reach the threshold of training iterations. This embodiment has improved the model updating efficiency.

Method and apparatus for updating deep learning model

A system comprises: a data warehouse, a storage device and a cluster including a plurality of computing nodes; the data warehouse is configured to store task data obtained from the user; at least one computing node in the cluster includes a resource scheduling component, and is configured to perform resource scheduling for the task and determine a computing node executing the task; the computing node executing the task comprises a model training component and/or a prediction component; the model training component is configured to, according to task data, invoke a corresponding type of learning model from the storage device; use sample data and training target included in the task data to train the learning model, to obtain the prediction model corresponding to the task and store the prediction model in the storage device; the prediction component is configured to obtain a prediction result output by the prediction model.

Intelligent big data system, and method and apparatus for providing intelligent big data service

An integrated facial recognition method and system. The method includes: receiving a request for acquiring an integrated facial recognition service sent by a user terminal, which includes: an identifier of a user-selected model associated with facial recognition of the user, and an identifier of an operation selected by the user from candidate operations; and executing distributedly an operation selected by the user from the candidate operations on the user-selected model associated with the facial recognition of the user to obtain an operation result, and storing the operation result. The embodiment has realized completing the operations such as training a model or developing a facial recognition application, without the need of buying hardware and establishing a software environment by the user, thereby saving the development cost and improving the convenience of using the facial recognition service.

Integrated facial recognition method and system

The present disclosure provides a lens module and a mobile terminal. The lens module can include a lens component, a mounting bracket, a blade assembly movably connected to the mounting bracket, and a driving assembly connected to the blade assembly. The blade assembly is provided with at least two apertures, and the at least two apertures have different diameters. The lens component is mounted to the mounting bracket, and the blade assembly is configured to rotate and switch the apertures to correspond to the lens component under driving of the driving assembly, such that an axis of one of the apertures coincides with an optical axis of the lens component.

Lens module and mobile terminal

The present disclosure provides a deep learning assignment processing method and apparatus, a device and a storage medium. It is feasible to obtain the deep learning assignment submitted by the user in a predetermined manner, the predetermined manner comprising the web UI manner, then submit the deep learning assignment to the deep learning system so that the deep learning system runs the submitted deep learning assignment. As compared with the prior art, processing such as programming is not needed upon submitting the deep learning assignment in the solutions of the present disclosure, thereby simplifying the user's operations, improving the processing efficiency of the deep learning assignment, and accelerating the user's speed of developing deep learning.

Deep learning assignment processing method and apparatus, device and storage medium

A camera assembly includes a base, and two cameras located on the base. When the base makes the two cameras in a tilted state, an angle between a first plane and a second plane is greater than 0 degree and less than 180 degrees, and back surfaces of the two cameras are opposing each other. The first plane is a plane perpendicular to an optical axis direction of one of the two cameras, and the second plane is a plane perpendicular to an optical axis direction of the other one of the two cameras.

Camera assembly and terminal

A camera assembly includes a first driving structure, a second driving structure, a transmission and a camera module. The transmission can cooperate with the first driving structure to drive the camera module to reciprocate linearly in a preset direction, and the transmission can cooperate with the second driving structure to drive the camera module to rotate about an axis in the preset direction.

Camera assembly and electronic device

The present disclosure provides a method and apparatus for scheduling a cloud server. A specific implementation mode of the method comprises: monitoring whether current time is in a first pre-set time period; in response to the monitoring that the current time is in the first pre-set time period, scheduling a cloud server in a first cloud server cluster having a running state being an idle state, as a target cloud server, to a second cloud server cluster, so that the target cloud server executes a task obtained by the second cloud server cluster; monitoring whether the current time is in a second pre-set time period; in response to the monitoring that the current time is in the second pre-set time period, rescheduling the target cloud server to the first cloud server cluster, so that the target cloud server executes a task obtained by the first cloud server cluster.

Method and apparatus for scheduling cloud server

Growing community of inventors

Beijing, China

Kai Zhou